© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 226 DeepSecure: A Real-Time Deep Learning-Based System for Enhancing Cybersecurity in Social Media through DeepFake Detection using LSTM and ResNext CNN Nikhil Dhiman 1 , Nitesh Sharma 2 , Vikalp Vashisth 3 ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - With the exponential rise of DeepFake content circulating on social media platforms like Twitter, the need for robust and real-time detection systems has become paramount to safeguard digital trust and authenticity. In response to this pressing concern, "DeepSecure," an innovative deep learning-based solution tailored for efficient DeepFake detection. By harnessing the power of Long Short-Term Memory (LSTM) networks [1] and Residual Next (ResNext) Convolutional Neural Networks (CNNs), DeepSecure adeptly analyzes multimedia content on social media feeds. The proposed system empowers users and platform administrators to combat the escalating threat of deceptive content, thereby fortifying the cybersecurity landscape in social media ecosystems. Through rigorous experimentation and real-world implementation, this research endeavors to offer a reliable and timely defense against the proliferation of DeepFake content on popular social media platforms. Key Words: DeepSecure, Real-Time Deep Learning, Cybersecurity, Social Media, DeepFake Detection, LSTM, ResNext CNN, Enhancing, Detection System, Digital Security 1. INTRODUCTION With the unprecedented growth of social media and the widespread sharing of multimedia content, the rise of DeepFake technology has emerged as a significant cybersecurity threat, jeopardizing the authenticity and trustworthiness of information circulated online. DeepFake techniques employ advanced machine learning algorithms, including Long Short-Term Memory (LSTM) [1] networks and Residual Next (ResNext) Convolutional Neural Networks (CNNs) [2], to produce highly realistic and deceptive fake videos and images. As the prevalence of DeepFake content continues to escalate on social media platforms, there is a pressing need for real-time, robust, and efficient solutions to detect and mitigate the dissemination of false and misleading information. In this research paper, we present "DeepSecure," a pioneering real-time deep learning-based system that uses the power of LSTM networks [1], Python, and ResNext CNNs [2] to effectively combat DeepFake threats in the realm of social media. DeepSecure is meticulously engineered to intelligently analyze multimedia content shared on social media platforms, enabling rapid and accurate identification of DeepFake content. Leveraging the strength of LSTM [1] and ResNext CNN [2] architectures, our system enhances cybersecurity measures by providing a reliable and scalable solution to counteract the growing sophistication of DeepFake technology. Through rigorous experimentation and performance evaluation, we aim to demonstrate the effectiveness and practicality of DeepSecure in safeguarding the integrity and trustworthiness of multimedia content on social media. The integration of deep learning techniques and Python programming enables DeepSecure to operate in real-time, allowing for swift detection and response to potential DeepFake threats. By presenting a comprehensive analysis of DeepSecure's capabilities, we endeavor to contribute valuable insights towards the ongoing efforts to mitigate the adverse impact of DeepFake content on social media platforms, fostering a safer and more secure digital landscape for all users. 2. LITERATURE SURVEY These days, a number of new threads are emerging as the usage of AI technology grows. A media or video can be edited using deep learning to create a false version and raise security concerns on social media sites. The changed media may be utilized for journalism, entertainment, and politics. Some excellent materials, such as an IEEE (Spectrum) publication [3], help to improve the quality of Deepfake development and leads to more fake content over the social media. Numerous research endeavors have been dedicated to the detection of deepfakes; however, achieving real-time detection remains a challenging pursuit. This research paper seeks to address this crucial gap by focusing on the development of a real-time deep learning-based system for robust deepfake detection. By exploring the fusion of LSTM [1] and ResNext CNN [2] models, our study aims to contribute to the advancement of cybersecurity in social media, enabling swift and efficient identification of manipulated content in a dynamically evolving digital landscape. In conclusion, the realm of deepfake detection has witnessed extensive research efforts, yet the challenge of real-time detection persists. This research paper takes a International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072